Developing HEV Control Systems

Summary

This video provides an in-depth exploration of developing and implementing control algorithms for hybrid electric vehicles (HEVs), focusing on energy management and drivability. It discusses regenerative braking control, emphasizing the blend of motor and mechanical braking to maximize energy recapture. Additionally, the video covers power management, ensuring battery power remains within safe limits. Testing methodologies for controllers using various drive cycles and battery state of charge (SOC) scenarios are outlined. Best practices for HEV control development are also shared, highlighting the challenges of balancing energy efficiency and vehicle performance.

Takeaways

• 😀 The regenerative braking algorithm aims to recapture kinetic energy effectively during braking.
• 🚗 A blending algorithm controls both motor regenerative braking torque and mechanical friction braking torque together.
• ⚙️ The estimated braking torque at the wheels is derived from brake pedal requests and disc brake equations.
• 🔋 The maximum motor regenerative braking torque is calculated based on the motor torque versus speed curve and the vehicle's transmission system.
• 🛑 Low-speed and SOC-based charge limit cut-offs ensure safe braking limits to prevent overheating and battery damage.
• ⚡ Power management maintains instantaneous battery power within dynamic discharge and charge limits set by the Battery Management System (BMS).
• 📊 The algorithm converts mechanical torque requests to electrical power requests using an efficiency map.
• 🚀 Testing new control algorithms is crucial; it's recommended to use the full system-level model for closed-loop control development.
• 🔍 Robustness testing involves using various drive cycles with different initial battery state of charge (SOC) values.
• 📈 Best practices for HEV controls include using reference applications, organizing functions, and testing new functions in closed-loop setups.

Q & A

What is the main purpose of regenerative braking in hybrid electric vehicles (HEVs)?

The main purpose of regenerative braking in HEVs is to recapture as much kinetic energy as possible during braking, which can then be used to recharge the vehicle's battery.

How does the blending algorithm work in regenerative braking control?

The blending algorithm works by controlling both the motor regenerative braking torque and the mechanical friction braking torque together to provide the necessary braking force while maximizing energy recovery.

What factors are considered when calculating the maximum motor regenerative braking torque?

The maximum motor regenerative braking torque is calculated using the maximum motor torque versus speed curve and is reflected at the wheels by the overall gear ratio of the transmission and differential.

What role does the Battery Management System (BMS) play in power management for HEVs?

The BMS dictates the amount of power that can be discharged or charged based on the battery's state of charge and temperature, ensuring that the battery operates within its dynamic power limits.

How does the algorithm ensure that electrical power requests stay within BMS limits?

The algorithm checks if the electrical power request is within the dynamic BMS power limits. If it exceeds these limits, the algorithm adjusts the mechanical power and torque requests accordingly.

What is the significance of testing HEV control algorithms frequently?

Frequent testing of HEV control algorithms is important to ensure their robustness and reliability across different driving conditions and battery states, which helps to optimize vehicle performance.

What metrics should be monitored during the testing of an HEV controller?

During testing, key metrics to monitor include engine and motor speeds and torques, battery state of charge (SOC) behavior, and overall vehicle performance such as fuel efficiency.

What best practices should be followed when developing HEV controls?

Best practices include using Powertrain Block Set reference applications as starting points, organizing functions into subsystems, utilizing unit tests, and combining aspects of both rule-based and optimal control methods.

What challenges does the presenter identify in the development of HEV control algorithms?

The presenter identifies the challenge of managing energy effectively while ensuring good drivability as the biggest hurdle in the design of HEV control algorithms.

What future topics will the presenter cover regarding HEV design?

The presenter will cover HEV design optimization exercises, including techniques to improve rule-based controllers used in HEV systems.